


import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


lowerCAmelCase__			:      Union[str, Any]								=trt.Logger(trt.Logger.WARNING)
lowerCAmelCase__			:      int								=absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

lowerCAmelCase__			:      int								=logging.getLogger(__name__)

lowerCAmelCase__			:      Tuple								=argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '--onnx_model_path',
    default=None,
    type=str,
    required=True,
    help='Path to ONNX model: ',
)

parser.add_argument(
    '--output_dir',
    default=None,
    type=str,
    required=True,
    help='The output directory where the model checkpoints and predictions will be written.',
)

# Other parameters

parser.add_argument(
    '--tokenizer_name',
    default='',
    type=str,
    required=True,
    help='Pretrained tokenizer name or path if not the same as model_name',
)

parser.add_argument(
    '--version_2_with_negative',
    action='store_true',
    help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
    '--null_score_diff_threshold',
    type=float,
    default=0.0,
    help='If null_score - best_non_null is greater than the threshold predict null.',
)

parser.add_argument(
    '--max_seq_length',
    default=3_84,
    type=int,
    help=(
        'The maximum total input sequence length after WordPiece tokenization. Sequences '
        'longer than this will be truncated, and sequences shorter than this will be padded.'
    ),
)
parser.add_argument(
    '--doc_stride',
    default=1_28,
    type=int,
    help='When splitting up a long document into chunks, how much stride to take between chunks.',
)

parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')

parser.add_argument(
    '--n_best_size',
    default=20,
    type=int,
    help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
    '--max_answer_length',
    default=30,
    type=int,
    help=(
        'The maximum length of an answer that can be generated. This is needed because the start '
        'and end predictions are not conditioned on one another.'
    ),
)

parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')

parser.add_argument(
    '--dataset_name',
    type=str,
    default=None,
    required=True,
    help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--dataset_config_name',
    type=str,
    default=None,
    help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
    '--fp16',
    action='store_true',
    help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
    '--int8',
    action='store_true',
    help='Whether to use INT8',
)

lowerCAmelCase__			:      int								=parser.parse_args()

if args.tokenizer_name:
				lowerCAmelCase__			:      Union[str, Any]								=AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
				raise ValueError(
				    'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
				    'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
				)

logger.info('Training/evaluation parameters %s', args)

lowerCAmelCase__			:      int								=args.per_device_eval_batch_size

lowerCAmelCase__			:      Any								=(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowerCAmelCase__			:      Optional[Any]								=True

lowerCAmelCase__			:      List[str]								='temp_engine/bert-fp32.engine'
if args.fpaa:
				lowerCAmelCase__			:      int								='temp_engine/bert-fp16.engine'
if args.inta:
				lowerCAmelCase__			:      Union[str, Any]								='temp_engine/bert-int8.engine'

# import ONNX file
if not os.path.exists('temp_engine'):
				os.makedirs('temp_engine')

lowerCAmelCase__			:      List[str]								=1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
				with open(args.onnx_model_path, 'rb') as model:
								if not parser.parse(model.read()):
												for error in range(parser.num_errors):
																print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
				lowerCAmelCase__			:      str								=[network.get_input(i) for i in range(network.num_inputs)]
				lowerCAmelCase__			:      Tuple								=[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

				with builder.create_builder_config() as config:
								lowerCAmelCase__			:      Optional[Any]								=1 << 50
								if STRICT_TYPES:
												config.set_flag(trt.BuilderFlag.STRICT_TYPES)
								if args.fpaa:
												config.set_flag(trt.BuilderFlag.FPaa)
								if args.inta:
												config.set_flag(trt.BuilderFlag.INTa)
								lowerCAmelCase__			:      Optional[Any]								=builder.create_optimization_profile()
								config.add_optimization_profile(profile)
								for i in range(len(input_names)):
												profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
								lowerCAmelCase__			:      Optional[int]								=builder.build_engine(network, config)

								# serialize_engine and store in file (can be directly loaded and deserialized):
								with open(engine_name, 'wb') as f:
												f.write(engine.serialize())






def 						a__			(				A__,      A__,      A__,      A__,      A__,      A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					np.asarray(inputs['input_ids'],      dtype=np.intaa				)
			SCREAMING_SNAKE_CASE_		:     Tuple					=					np.asarray(inputs['attention_mask'],      dtype=np.intaa				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					np.asarray(inputs['token_type_ids'],      dtype=np.intaa				)

			# Copy inputs
			cuda.memcpy_htod_async(d_inputs[0],      input_ids.ravel(),      __lowerCAmelCase				)
			cuda.memcpy_htod_async(d_inputs[1],      attention_mask.ravel(),      __lowerCAmelCase				)
			cuda.memcpy_htod_async(d_inputs[2],      token_type_ids.ravel(),      __lowerCAmelCase				)
			# start time
			SCREAMING_SNAKE_CASE_		:     Dict					=					time.time()
			# Run inference
			context.execute_async(
			    bindings=[int(__lowerCAmelCase				) for d_inp in d_inputs] + [int(__lowerCAmelCase				), int(__lowerCAmelCase				)],      stream_handle=stream.handle				)
			# Transfer predictions back from GPU
			cuda.memcpy_dtoh_async(__lowerCAmelCase,      __lowerCAmelCase,      __lowerCAmelCase				)
			cuda.memcpy_dtoh_async(__lowerCAmelCase,      __lowerCAmelCase,      __lowerCAmelCase				)
			# Synchronize the stream and take time
			stream.synchronize()
			# end time
			SCREAMING_SNAKE_CASE_		:     str					=					time.time()
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					end_time - start_time
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					(h_outputa, h_outputa)
			# print(outputs)
			return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
lowerCAmelCase__			:      Optional[Any]								=Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
				datasets.utils.logging.set_verbosity_warning()
				transformers.utils.logging.set_verbosity_info()
else:
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
				set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
				# Downloading and loading a dataset from the hub.
				lowerCAmelCase__			:      Optional[int]								=load_dataset(args.dataset_name, args.dataset_config_name)
else:
				raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

lowerCAmelCase__			:      Any								=raw_datasets['validation'].column_names

lowerCAmelCase__			:      List[str]								='question' if 'question' in column_names else column_names[0]
lowerCAmelCase__			:      Any								='context' if 'context' in column_names else column_names[1]
lowerCAmelCase__			:      Union[str, Any]								='answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
lowerCAmelCase__			:      str								=tokenizer.padding_side == 'right'

if args.max_seq_length > tokenizer.model_max_length:
				logger.warning(
				    F"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
				    F"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
				)

lowerCAmelCase__			:      Union[str, Any]								=min(args.max_seq_length, tokenizer.model_max_length)






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					[q.lstrip() for q in examples[question_column_name]]

			# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
			# in one example possible giving several features when a context is long, each of those features having a
			# context that overlaps a bit the context of the previous feature.
			SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer(
			    examples[question_column_name if pad_on_right else context_column_name],      examples[context_column_name if pad_on_right else question_column_name],      truncation='only_second' if pad_on_right else 'only_first',      max_length=__lowerCAmelCase,      stride=args.doc_stride,      return_overflowing_tokens=__lowerCAmelCase,      return_offsets_mapping=__lowerCAmelCase,      padding='max_length',      )

			# Since one example might give us several features if it has a long context, we need a map from a feature to
			# its corresponding example. This key gives us just that.
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tokenized_examples.pop('overflow_to_sample_mapping'				)

			# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
			# corresponding example_id and we will store the offset mappings.
			SCREAMING_SNAKE_CASE_		:     Dict					=					[]

			for i in range(len(tokenized_examples['input_ids']				)				):
						# Grab the sequence corresponding to that example (to know what is the context and what is the question).
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenized_examples.sequence_ids(__lowerCAmelCase				)
						SCREAMING_SNAKE_CASE_		:     Dict					=					1 if pad_on_right else 0

						# One example can give several spans, this is the index of the example containing this span of text.
						SCREAMING_SNAKE_CASE_		:     Any					=					sample_mapping[i]
						tokenized_examples["example_id"].append(examples['id'][sample_index]				)

						# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
						# position is part of the context or not.
						SCREAMING_SNAKE_CASE_		:     Dict					=					[
						    (o if sequence_ids[k] == context_index else None)
						    for k, o in enumerate(tokenized_examples['offset_mapping'][i]				)
						]

			return tokenized_examples


lowerCAmelCase__			:      List[str]								=raw_datasets['validation']
# Validation Feature Creation
lowerCAmelCase__			:      str								=eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='Running tokenizer on validation dataset',
)

lowerCAmelCase__			:      Optional[Any]								=default_data_collator

lowerCAmelCase__			:      Dict								=eval_dataset.remove_columns(['example_id', 'offset_mapping'])
lowerCAmelCase__			:      Dict								=DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)






def 						a__			(				A__,      A__,      A__,      A__="eval"				):
			SCREAMING_SNAKE_CASE_		:     str					=					postprocess_qa_predictions(
			    examples=__lowerCAmelCase,      features=__lowerCAmelCase,      predictions=__lowerCAmelCase,      version_2_with_negative=args.version_2_with_negative,      n_best_size=args.n_best_size,      max_answer_length=args.max_answer_length,      null_score_diff_threshold=args.null_score_diff_threshold,      output_dir=args.output_dir,      prefix=__lowerCAmelCase,      )
			# Format the result to the format the metric expects.
			if args.version_2_with_negative:
						SCREAMING_SNAKE_CASE_		:     str					=					[
						    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
						]
			else:
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

			SCREAMING_SNAKE_CASE_		:     List[Any]					=					[{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
			return EvalPrediction(predictions=__lowerCAmelCase,      label_ids=__lowerCAmelCase				)


lowerCAmelCase__			:      int								=load_metric('squad_v2' if args.version_2_with_negative else 'squad')

# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
				# setup for TRT inferrence
				for i in range(len(input_names)):
								context.set_binding_shape(i, INPUT_SHAPE)
				assert context.all_binding_shapes_specified






				def 						a__			(				A__				):
							return trt.volume(engine.get_binding_shape(__lowerCAmelCase				)				) * engine.get_binding_dtype(__lowerCAmelCase				).itemsize

				# Allocate device memory for inputs and outputs.
				lowerCAmelCase__			:      List[Any]								=[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

				# Allocate output buffer
				lowerCAmelCase__			:      Tuple								=cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
				lowerCAmelCase__			:      Optional[Any]								=cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
				lowerCAmelCase__			:      List[str]								=cuda.mem_alloc(h_outputa.nbytes)
				lowerCAmelCase__			:      List[str]								=cuda.mem_alloc(h_outputa.nbytes)

				# Create a stream in which to copy inputs/outputs and run inference.
				lowerCAmelCase__			:      int								=cuda.Stream()

				# Evaluation
				logger.info('***** Running Evaluation *****')
				logger.info(F"""  Num examples = {len(eval_dataset)}""")
				logger.info(F"""  Batch size = {args.per_device_eval_batch_size}""")

				lowerCAmelCase__			:      List[str]								=0.0
				lowerCAmelCase__			:      Dict								=0
				lowerCAmelCase__			:      Tuple								=timeit.default_timer()

				lowerCAmelCase__			:      Any								=None
				for step, batch in enumerate(eval_dataloader):
								lowerCAmelCase__							,lowerCAmelCase__			:      Tuple								=model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
								total_time += infer_time
								niter += 1

								lowerCAmelCase__							,lowerCAmelCase__			:      List[str]								=outputs
								lowerCAmelCase__			:      List[Any]								=torch.tensor(start_logits)
								lowerCAmelCase__			:      List[str]								=torch.tensor(end_logits)

								# necessary to pad predictions and labels for being gathered
								lowerCAmelCase__			:      str								=accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
								lowerCAmelCase__			:      Tuple								=accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

								lowerCAmelCase__			:      Optional[int]								=(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
								lowerCAmelCase__			:      Optional[Any]								=logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

				if all_preds is not None:
								lowerCAmelCase__			:      str								=nested_truncate(all_preds, len(eval_dataset))

				lowerCAmelCase__			:      str								=timeit.default_timer() - start_time
				logger.info('  Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
				# Inference time from TRT
				logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 10_00 / niter))
				logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 10_00))



				logger.info('Total Number of Inference =  %d', niter)

lowerCAmelCase__			:      int								=post_processing_function(eval_examples, eval_dataset, all_preds)
lowerCAmelCase__			:      Optional[int]								=metric.compute(predictions=prediction.predictions, references=prediction.label_ids)



logger.info(F"""Evaluation metrics: {eval_metric}""")






"""simple docstring"""


from __future__ import annotations

import math





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if len(__lowerCAmelCase							) != 2 or len(a[0]							) != 2 or len(__lowerCAmelCase							) != 2 or len(b[0]							) != 2:
												raise Exception("Matrices are not 2x2"							)
						lowerCamelCase__   =[
						    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
						    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
						]
						return new_matrix





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Dict:




						'''simple docstring'''



						return [
						    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
						    for row in range(len(__lowerCAmelCase							)							)
						]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   List[Any]:




						'''simple docstring'''



						return [
						    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
						    for row in range(len(__lowerCAmelCase							)							)
						]





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple[list, list, list, list]:




						'''simple docstring'''



						if len(__lowerCAmelCase							) % 2 != 0 or len(a[0]							) % 2 != 0:
												raise Exception("Odd matrices are not supported!"							)

						lowerCamelCase__   =len(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_length // 2

						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase  ,       __lowerCAmelCase							)] for i in range(__lowerCAmelCase							)]
						lowerCamelCase__   =[
						    [a[i][j] for j in range(__lowerCAmelCase  ,       __lowerCAmelCase							)] for i in range(__lowerCAmelCase  ,       __lowerCAmelCase							)
						]

						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase							)] for i in range(__lowerCAmelCase							)]
						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase							)] for i in range(__lowerCAmelCase  ,       __lowerCAmelCase							)]

						return top_left, top_right, bot_left, bot_right





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple[int, int]:




						'''simple docstring'''



						return len(__lowerCAmelCase							), len(matrix[0]							)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   None:




						'''simple docstring'''



						print("\n".join(str(__lowerCAmelCase							) for line in matrix							)							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if matrix_dimensions(__lowerCAmelCase							) == (2, 2):
												return default_matrix_multiplication(__lowerCAmelCase  ,       __lowerCAmelCase							)

						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =split_matrix(__lowerCAmelCase							)
						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =split_matrix(__lowerCAmelCase							)

						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)

						lowerCamelCase__   =matrix_addition(matrix_subtraction(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_subtraction(matrix_subtraction(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)

						# construct the new matrix from our 4 quadrants
						lowerCamelCase__   =[]
						for i in range(len(__lowerCAmelCase							)							):
												new_matrix.append(top_left[i] + top_right[i]							)
						for i in range(len(__lowerCAmelCase							)							):
												new_matrix.append(bot_left[i] + bot_right[i]							)
						return new_matrix





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if matrix_dimensions(__lowerCAmelCase							)[1] != matrix_dimensions(__lowerCAmelCase							)[0]:
												lowerCamelCase__   =(
												    "Unable to multiply these matrices, please check the dimensions.\n"
												    F'''Matrix A: {matrixa}\n'''
												    F'''Matrix B: {matrixa}'''
												)
												raise Exception(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_dimensions(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_dimensions(__lowerCAmelCase							)

						if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
												return [matrixa, matrixa]

						lowerCamelCase__   =max(*__lowerCAmelCase  ,       *__lowerCAmelCase							)
						lowerCamelCase__   =int(math.pow(2  ,       math.ceil(math.loga(__lowerCAmelCase							)							)							)							)
						lowerCamelCase__   =matrixa
						lowerCamelCase__   =matrixa

						# Adding zeros to the matrices so that the arrays dimensions are the same and also
						# power of 2
						for i in range(0  ,       __lowerCAmelCase							):
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								new_matrixa[i].append(0							)
												else:
																		new_matrixa.append([0] * maxim							)
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								new_matrixa[i].append(0							)
												else:
																		new_matrixa.append([0] * maxim							)

						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       __lowerCAmelCase							)

						# Removing the additional zeros
						for i in range(0  ,       __lowerCAmelCase							):
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								final_matrix[i].pop()
												else:
																		final_matrix.pop()
						return final_matrix


if __name__ == "__main__":
	a				=[
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 2, 3, 1],
	]
	a				=[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
	print(strassen(matrixa, matrixa))


